---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cohere_generated_abstracts_roberta
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# cohere_generated_abstracts_roberta

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co./FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0078        | 0.0838 | 100  | 0.0029          | 0.9996   |
| 0.0036        | 0.1676 | 200  | 0.0053          | 0.9992   |
| 0.0064        | 0.2515 | 300  | 0.0012          | 0.9999   |
| 0.002         | 0.3353 | 400  | 0.0028          | 0.9996   |
| 0.0019        | 0.4191 | 500  | 0.0009          | 0.9999   |
| 0.0014        | 0.5029 | 600  | 0.0026          | 0.9998   |
| 0.0003        | 0.5868 | 700  | 0.0012          | 0.9999   |
| 0.0017        | 0.6706 | 800  | 0.0000          | 1.0      |
| 0.0015        | 0.7544 | 900  | 0.0000          | 1.0      |
| 0.0019        | 0.8382 | 1000 | 0.0007          | 0.9999   |
| 0.0033        | 0.9220 | 1100 | 0.0048          | 0.9994   |
| 0.0013        | 1.0059 | 1200 | 0.0001          | 1.0      |
| 0.0032        | 1.0897 | 1300 | 0.0015          | 0.9998   |
| 0.0013        | 1.1735 | 1400 | 0.0000          | 1.0      |
| 0.0           | 1.2573 | 1500 | 0.0000          | 1.0      |
| 0.0           | 1.3412 | 1600 | 0.0000          | 1.0      |
| 0.0           | 1.4250 | 1700 | 0.0000          | 1.0      |
| 0.0003        | 1.5088 | 1800 | 0.0023          | 0.9996   |
| 0.0005        | 1.5926 | 1900 | 0.0000          | 1.0      |
| 0.0           | 1.6764 | 2000 | 0.0000          | 1.0      |
| 0.0           | 1.7603 | 2100 | 0.0000          | 1.0      |
| 0.0           | 1.8441 | 2200 | 0.0000          | 1.0      |
| 0.0           | 1.9279 | 2300 | 0.0000          | 1.0      |
| 0.0           | 2.0117 | 2400 | 0.0000          | 1.0      |
| 0.0           | 2.0956 | 2500 | 0.0000          | 1.0      |
| 0.0           | 2.1794 | 2600 | 0.0000          | 1.0      |
| 0.0           | 2.2632 | 2700 | 0.0000          | 1.0      |
| 0.0           | 2.3470 | 2800 | 0.0000          | 1.0      |
| 0.0           | 2.4308 | 2900 | 0.0000          | 1.0      |
| 0.0           | 2.5147 | 3000 | 0.0000          | 1.0      |
| 0.0           | 2.5985 | 3100 | 0.0000          | 1.0      |
| 0.0           | 2.6823 | 3200 | 0.0000          | 1.0      |
| 0.0           | 2.7661 | 3300 | 0.0000          | 1.0      |
| 0.0           | 2.8500 | 3400 | 0.0000          | 1.0      |
| 0.0           | 2.9338 | 3500 | 0.0000          | 1.0      |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1